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An improved method of locality-sensitive hashing for scalable instance matching

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Abstract

In this study, we propose a scalable approach for automatically identifying similar candidate instance pairs in very large datasets. Efficient candidate pair generation is an essential to many computational problems involving calculation of instance similarities. Calculating similarities of instances with a large number of properties and efficiently matching a large number of similar instances in a scalable way are two significant bottlenecks of candidate instance pair generation. In our approach, we utilize locality-sensitive hashing (LSH) technique to greatly improve the scalability of candidate instance pair generation. Based on the candidate similarity threshold, our algorithm automatically discovers the optimum number of hash functions in each band in LSH. Moreover, we evaluated the scalability of our approach and its effectiveness in instance matching task using real-world very large datasets.

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Notes

  1. http://bit.ly/LinstaMatch.

  2. www.mashable.com.

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Acknowledgements

We would like to thank the OAEI 2016 campaign Instance Matching Task organizers, particularly Dr. Manel Achichi, Dr. Daniel Faria and Dr. Ernesto Jimnez-Ruiz, for providing run time evaluations. Also, we thank Dr. Daniel Faria for providing AML’s OAEI 2016 version as a stand-alone JAR for testing purposes.

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Authors and Affiliations

  1. Department of Computer Science, Kent State University, Kent, OH, USA

    Mehmet Aydar

  2. Department of Software Engineering, Bahcesehir University, Beşiktaş, Istanbul, Turkey

    Serkan Ayvaz

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  1. Mehmet Aydar
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  2. Serkan Ayvaz
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Correspondence to Mehmet Aydar.

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Aydar, M., Ayvaz, S. An improved method of locality-sensitive hashing for scalable instance matching. Knowl Inf Syst 58, 275–294 (2019). https://doi.org/10.1007/s10115-018-1199-5

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